Functional traits affect the demographic performance of individuals in their environment, leading to fitness differences that scale up to drive population dynamics and community assembly. Understanding the links between traits and fitness is, therefore, critical for predicting how populations and communities respond to environmental change. However, the net effects of traits on species fitness are largely unknown because we have lacked a framework for estimating fitness across multiple species and environments. We present a modelling framework that integrates trait effects on demographic performance over the life cycles of individuals to estimate the net effect of traits on species fitness. This approach involves (1) modelling trait effects on individual demographic rates (growth, survival and recruitment) as multidimensional performance surfaces that vary with individual size and environment and (2) integrating these effects into a population model to project population growth rates (i.e., fitness) as a function of traits and environment. We illustrate our approach by estimating performance surfaces and fitness landscapes for trees across a temperature gradient in the eastern United States. Functional traits (wood density, specific leaf area and maximum height) interacted with individual size and temperature to influence tree growth, survival and recruitment rates, generating demographic trade‐offs and shaping the contours of fitness landscapes. Tall tree species had high survival, growth and fitness across the temperature gradient. Wood density and specific leaf area had interactive effects on demographic performance, resulting in fitness landscapes with multiple peaks. With this approach it is now possible to empirically estimate the net effect of traits on fitness, leading to an improved understanding of the selective forces that drive community assembly and permitting generalizable predictions of population and community dynamics in changing environments.
- Award ID(s):
- 1906243
- PAR ID:
- 10225426
- Date Published:
- Journal Name:
- Trends in ecology evolution
- Volume:
- 35
- Issue:
- 11
- ISSN:
- 0169-5347
- Page Range / eLocation ID:
- 1037-1047
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.tree.2020.07.010
